Using Agents to Build Other Agents: How to Scale Your AI Infrastructure
The Fund Manager’s AI Playbook | Part 7 of 8
Once you have one agent running smoothly, a natural question emerges: what if different parts of your operation had their own dedicated agents?
This isn’t a hypothetical. It’s how the most sophisticated AI-powered operations are being built right now - and the concept is simpler than it sounds.
Why Specialization Beats a Single Generalist Agent
Your first agent is probably a generalist - it handles a wide range of tasks and has broad context about your business. That’s the right starting point. But as your operation grows, specialization starts to matter.
Consider the difference between asking one person to run your investor relations, manage your social media, monitor your CRM, and handle your scheduling - versus having a dedicated person for each. The specialists go deeper, develop better judgment in their domain, and don’t drop things when one area gets busy.
The same is true for agents.
What a Multi-Agent Operation Can Look Like
A mature AI infrastructure for a fund might include:
A CRM Agent - owns contact hygiene, pipeline management, tagging, and follow-up sequencing (as we covered in Part 5)
A Content Agent - manages your social media calendar, drafts posts, monitors engagement, and flags comments worth responding to (Part 3)
An IR Agent - handles investor communication cadences, drafts updates, and manages the LP communication pipeline (Part 6)
A Research Agent - monitors market developments, researches prospective LPs, and surfaces relevant information before you need it
A Coordinator Agent - your primary interface, which receives your instructions and delegates tasks to the appropriate specialist
These agents work in parallel, each running its own domain without creating noise for the others.
How Agents Build and Manage Other Agents
Modern agent platforms allow a “parent” agent to spin up and configure new agents on demand. In practice, this means:
You describe the role you need to your primary agent (“I need a dedicated agent to handle all social media for our new fund launch”)
The primary agent drafts the instructions, sets the memory structure, and creates the new agent
The new agent is live and connected - ready to receive tasks and report back
You don’t need to write instructions from scratch each time. Your primary agent knows your business well enough to brief a new specialist appropriately. You review and approve; the agent handles the configuration.
Communication Between Agents
One of the most powerful capabilities in a multi-agent system is inter-agent communication. Your CRM agent can alert your IR agent when a high-value prospect reaches a certain pipeline stage. Your content agent can notify your coordinator when a post generates unusual engagement. Your research agent can brief your IR agent before an LP call.
These handoffs happen automatically, in the background, without you orchestrating each one. The system operates like a small team that’s always in sync.
Oversight Without Micromanagement
The more agents you have running, the more important it is to have a clear oversight structure. A few principles:
One agent is your primary interface. Everything important surfaces through it. You shouldn’t be tracking six separate agent conversations.
Agents report on completion, not on every step. You want outcomes, not play-by-play. Build this into their instructions.
Escalation rules are explicit. Every agent should know what it can decide on its own, what it should flag, and what it should never do without approval.
Done right, a multi-agent system requires less of your attention than a single generalist agent, because each specialist is better at its job and generates fewer errors that need your intervention.
Starting Simple
You don’t build this all at once. You start with one agent, prove out the value, then expand. Each new agent you add should solve a specific problem you’ve already identified - not create infrastructure for problems you don’t have yet.
The managers who build this incrementally end up with robust, reliable systems. The ones who try to build everything at once end up with complicated systems nobody understands.
Next up: Stop Choosing - How OpenRouter Gives You Every AI Model for the Right Job at the Right Price
Interested in building a multi-agent infrastructure for your fund? Book a free call or visit gaml-e.com to talk through what makes sense for your operation.
